train_lm.sh
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#!/bin/bash
# Copyright 2016 Vincent Nguyen
# 2016 Johns Hopkins University (author: Daniel Povey)
# 2017 Ashish Arora
# 2017 Hossein Hadian
# Apache 2.0
#
# This script trains a LM on the YOMDLE training transcriptions.
# It is based on the example scripts distributed with PocoLM
# It will check if pocolm is installed and if not will proceed with installation
set -e
stage=0
dir=data/local/local_lm
data_dir=data
echo "$0 $@" # Print the command line for logging
. ./utils/parse_options.sh || exit 1;
lm_dir=${dir}/data
mkdir -p $dir
. ./path.sh || exit 1; # for KALDI_ROOT
export PATH=$KALDI_ROOT/tools/pocolm/scripts:$PATH
( # First make sure the pocolm toolkit is installed.
cd $KALDI_ROOT/tools || exit 1;
if [ -d pocolm ]; then
echo Not installing the pocolm toolkit since it is already there.
else
echo "$0: Please install the PocoLM toolkit with: "
echo " cd ../../../tools; extras/install_pocolm.sh; cd -"
exit 1;
fi
) || exit 1;
bypass_metaparam_optim_opt=
# If you want to bypass the metaparameter optimization steps with specific metaparameters
# un-comment the following line, and change the numbers to some appropriate values.
# You can find the values from output log of train_lm.py.
# These example numbers of metaparameters is for 4-gram model (with min-counts)
# running with train_lm.py.
# The dev perplexity should be close to the non-bypassed model.
#bypass_metaparam_optim_opt=
# Note: to use these example parameters, you may need to remove the .done files
# to make sure the make_lm_dir.py be called and tain only 3-gram model
#for order in 3; do
#rm -f ${lm_dir}/${num_word}_${order}.pocolm/.done
if [ $stage -le 0 ]; then
mkdir -p ${dir}/data
mkdir -p ${dir}/data/text
echo "$0: Getting the Data sources"
rm ${dir}/data/text/* 2>/dev/null || true
# Note: the name 'dev' is treated specially by pocolm, it automatically
# becomes the dev set.
nr=`cat $data_dir/train/text | wc -l`
nr_dev=$(($nr / 10 ))
nr_train=$(( $nr - $nr_dev ))
# use the training data as an additional data source.
# we can later fold the dev data into this.
head -n $nr_train $data_dir/train/text | cut -d " " -f 2- > ${dir}/data/text/train.txt
tail -n $nr_dev $data_dir/train/text | cut -d " " -f 2- > ${dir}/data/text/dev.txt
# for reporting perplexities, we'll use the "real" dev set.
# (the validation data is used as ${dir}/data/text/dev.txt to work
# out interpolation weights.)
# note, we can't put it in ${dir}/data/text/, because then pocolm would use
# it as one of the data sources.
cut -d " " -f 2- < $data_dir/test/text > ${dir}/data/real_dev_set.txt
# get the wordlist from MADCAT text
cat ${dir}/data/text/train.txt | tr '[:space:]' '[\n*]' | grep -v "^\s*$" | sort | uniq -c | sort -bnr > ${dir}/data/word_count
cat ${dir}/data/word_count | awk '{print $2}' > ${dir}/data/wordlist
fi
order=3
if [ $stage -le 1 ]; then
# decide on the vocabulary.
# Note: you'd use --wordlist if you had a previously determined word-list
# that you wanted to use.
# Note: if you have more than one order, use a certain amount of words as the
# vocab and want to restrict max memory for 'sort',
echo "$0: training the unpruned LM"
min_counts='train=1'
wordlist=${dir}/data/wordlist
lm_name="`basename ${wordlist}`_${order}"
if [ -n "${min_counts}" ]; then
lm_name+="_`echo ${min_counts} | tr -s "[:blank:]" "_" | tr "=" "-"`"
fi
unpruned_lm_dir=${lm_dir}/${lm_name}.pocolm
train_lm.py --wordlist=${wordlist} --num-splits=5 --warm-start-ratio=1 \
--min-counts="$min_counts" \
--limit-unk-history=true \
${bypass_metaparam_optim_opt} \
${dir}/data/text ${order} ${lm_dir}/work ${unpruned_lm_dir}
get_data_prob.py ${dir}/data/real_dev_set.txt ${unpruned_lm_dir} 2>&1 | grep -F '[perplexity'
mkdir -p ${dir}/data/arpa
format_arpa_lm.py ${unpruned_lm_dir} | gzip -c > ${dir}/data/arpa/${order}gram_unpruned.arpa.gz
fi